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Artificial Immunity-Based Security Response Model for the Internet of Things

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Artificial Immunity-Based Security Response Model for the Internet of Things JOURNAL OF COMPUTERS, VOL. 8, NO. 12, DECEMBER 2013 3111 Artificial Immunity-based Security Response Model for the Internet of Things Caiming Liu School of Information Science & Technology, Southwest Jiaotong University, Chengdu, China School of Computer Science, Leshan Normal University, Leshan, China Email: [email protected] Yan Zhang*, Zongyin Cai School of Computer Science, Leshan Normal University, Leshan, China Email: [email protected] Jin Yang School of Computer Science, Leshan Normal University, Leshan, China School of Information Science & Technology, Southwest Jiaotong University, Chengdu, China Email: [email protected] Lingxi Peng Department of Computer and Education Software, Guangzhou University, Guangzhou, China Email: [email protected] Abstract—Rapid expansion of the Internet of Things (IoT) open surrounding [3] and not availably defended. Hostile caused more and more security problems and attacks in intruders can relatively easily attack IoT systems through particular. Secure IoT needs reasonable disposition after or accessing sense nodes. Massive data coming from or when being attacked. To meet the above requirements of going to the sense layer of IoT may suffer losses. The IoT security, a security response model for IoT based on security situation is not optimistic. artificial immune system is proposed in this paper. IoT data packets are captured and transformed into immune The security problems of IoT have attracted high antigens which are defined in the real IoT security attention of researchers. The current research is mainly environment. Recognizer is defined and simulative to focused on privacy protection [4, 5], security model [6, 7], recognize harmful antigens. The immune mechanisms of and etc. However, these security theories and antigen match, dynamic evolution of recognizer and self technologies were restricted to static defense concept. elements are simulative to adapt the real-time change of IoT. Flexible adaptation to real IoT security environment and Abnormal antigens are recognized and their danger value is reasonable response to real-time IoT security events are assessed. Strategy library of security response is constructed. urgent to be solved. Based on the danger of abnormal antigens which represent The security environment of IoT is changeful in real- specific IoT data, reasonable security response array is calculated to respond to attacks. Experiments are simulative time. The strategy of attack detection and response is not to realize proposed model. Their results show feasibility and immutable. Static response methods for attacks effectiveness in security response for IoT. threatening IoT applications have changeless strategy and can not meets the requirements of security response. Index Terms—Internet of Things, Artificial Immune System, Reasonable response theories and technologies needs to Security Response, Attack be adaptive to different situations. Intelligent computation measures are worth being taken into account to resolve above-mentioned problems. In this paper, Artificial I. INTRODUCTION Immune System (AIS) [8] which is one of the most active The Internet of Things (IoT) [1] is confronted with intelligent computation methods and has the attributes similar security problems to traditional computer self-learning and self-adaptation is used to resolve the networks [2]. In addition, it has its specific complicated adaptive response problems for IoT. security environment. A lot of sense nodes are exposed to AIS imitates the excellent principles and mechanisms of Biological Immune System (BIS). It has been a research hotspot in the fields of bionics and computation Manuscript received January 1, 2013; revised June 1, 2013; accepted intelligence. Since 2002, International Conference on July 1, 2013. Artificial Immune Systems has been held 11 times [9]. * Corresponding author, [email protected]. Based on the similarity between BIS and computer security issues, AIS was introduced into information © 2013 ACADEMY PUBLISHER doi:10.4304/jcp.8.12.3111-3118 3112 JOURNAL OF COMPUTERS, VOL. 8, NO. 12, DECEMBER 2013 security by relative researchers. There was much AIS B. Data Preprocessing based research literature [10, 11] in the fields of The original data to be analyzed comes from IoT information security and others in recent years. In the traffic. Fig. 2 illustrates how IoT packets are captured and traditional network security fields, AIS has been applied key data of IoT is got. AISRM captures IoT packets and into computer virus defense, intrusion detection, security extracts the packet head. Source IP address, target IP risk assessment, etc. address, label ID of things, card reader ID, data timestamp and etc that express packet signature are II. PROPOSED SECURITY RESPONSE MODEL extracted from the packet head. The proposed Artificial Immunity-based Security Response Model for the Internet of Things (AISRM) aims at recognizing and responding to attacks against IoT. It simulates the principles and mechanisms of AIS to be adaptive to security response for IoT. A. Architecture of Security Response The architecture of AISRM is shown in Fig. 1. It consists of 5 modules including data preprocessing, simulation of AIS principles, attack recognition, attack danger assessment and security response. Original IoT data is preprocessed and main signature information of IoT packets is got. AISRM simulates AIS data, recognizer and mechanisms to recognize attacks. The simulation makes it run the artificial immune principles in the real environment of IoT. Furthermore, it assesses the danger of recognized abnormal antigen. Moreover, it constructs strategy library of security response. Its ultimate goal is to respond to attacks reasonably. Figure 2. Process of Data Preprocessing. Let the signature set of packet head be SigHead which meets SigHead= { sIP,,,, dIP tID rID stmp } , where, sIP is the source IP address, dIP is the destination IP address, tID and rID means label ID of things and card reader ID, respectively, stmp is the time when the packet is got. Let the IoT data be IoTsig which is shown in Eq. (1). IoTsig =∈=∨∈{ ID, i ID N , i s1 ... sj ... s l s j {} 0,1 (1) ∨∈l N,( i = BinaryString h)∨∀∈ h SigHead} Where, ID is the serial number, i is the primitive data and consists of binary characters, N is nature number set, the function BinaryString( ) is used to transform the head information of IoT packets into binary strings, h is one of elements of SigHead. In Eq. (1), IoTsig contains the signature information of Figure 1. Architecture of AISRM. IoT packet. It comes from the original IoT data. It is generated by the process of data preprocessing which runs 4 steps including packets capturing, packet head extracting, signature extracting and binary string forming. The first step captures the IoT data which flows through IoT gateway and transportation gateway. The second step © 2013 ACADEMY PUBLISHER JOURNAL OF COMPUTERS, VOL. 8, NO. 12, DECEMBER 2013 3113 extracts the heads of IoT packets. The third step extracts the above relative memory, immature and mature signature information of packet head. Finally, the fourth recognizers is the same. It shows that they have the same step transforms IoT signature information into binary ancestors. The domain t exclusively belongs to memory strings which is used to judge whether IoT packets recognizers. It plus 1 makes the current thickness value contain attacks. after the recognizer recognizes a harmful antigen. C. Data Simulation E. Immune Mechanism Simulation To use the principles of AIS, data in the environment AISRM adopts artificial immune mechanisms to make of IoT needs to be simulated into antigen which is in recognizers and self elements can be self-adaptive to the immune style. Antigen in AISRM is defined as Def. 1. change of IoT environment. It takes advantage of dynamic strategy to evolve immune elements. It means Defenition 1. Let antigen set in IoT environment be A that immune elements may be different in different which meets A ===∧∀∈{a a l,. a iot i iot IoTsig }. The moment. Let the data set Ω in the beginning be Ω(0) . elements in A constitute the original data to be classified Let Ω at the moment t be Ω(t) . The simulation of by AISRM. artificial immune mechanisms is described in the following. Normal ones in A belongs to self set which is defined as S. Abnormal ones in A belongs to non-self set which is 1) Antigen Match defined as N. Non-self antigens come from IoT data which contains attacks. They hide in all antigens. AISRM In immune systems, when antigens touch immune cells, uses AIS principles to sort antigens input by the data immune cells recognize antigens through the antibodies preprocessing module into normal ones (self antigens) spreading outside the surface of the immune cells. To and abnormal ones (non-self antigens). Its final target is simulate the mechanism, a match method that recognizers to respond to non-self antigens which may threaten IoT recognize antigens is needed. Presently, feasible potentially. matching methods include Hamming, Euclidean, r- Contiguous, and etc [13]. Most existing literatures on AIS D. Recognizer Simulation based information security adopts r-Contiguous which In the immune system, immune cells are responsible judges whether a group binary string is the same between for recognizing harmful antigens. AISRM uses recognizer and antigen. Some effects of antigen matching recognizers to simulate immune cells to imitate the were achieved in some ways. However, some binary mechanism of the specificity recognition in AIS. characters are the same, but are not contiguous. The Recognizers dynamically evolve to detect attacks against proposed model improves the traditional r-Contiguous IoT. The data set of recognizer is defined as Def. 2. and constructs grouped r-Contiguous match algorithm. It calculates the sum of groups which have the same Defenition 2. Let the data set of recognizer be R which characters and are not near to each other. meets R=∈{ ant,, ag cnt ,,, tp t fam ant U ,, ag cnt ,,, tp t Grouped r-Contiguous match algorithm is used to judge whether recognizer matches antigen. It is shown in fam∈ I} , where, ant is the antibody string, I is Eq.
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